We first present a brief overview of the GBT motion planner from [1]}. There, a framework is developed for extending any kinodynamic tree-based motion planner to the belief space, where the edges are still nominal controllers and trajectories, but the nodes are Gaussian beliefs. The algorithm proceeds as follows: a belief is randomly sampled, its closest node is computed using the 2-Wasserstein distance, and extended by a random control input. The uncertainty covariance is propagated using the technique discussed in Sec. REF . A new node is only added to the tree if it satisfies the chance constraints of probability of collision with obstacles, which is conservatively approximated using [2]}, [3]}, [4]}. The process repeats until a solution is found.
